Find flow-based communities in complex networks
Use the map equation framework and Infomap to model how flow moves through your network and detect multilevel communities in directed, weighted, multilayer, bipartite, and memory networks.
import networkx as nx from infomap import find_communities G = nx.Graph([(1, 2), (1, 3), (2, 3), (3, 4), (4, 5), (4, 6), (5, 6)]) communities = find_communities(G) # [{1, 2, 3}, {4, 5, 6}]
Since 2008, the framework has grown from a random-walk coding idea into open-source software, visualization tools, and ongoing research on higher-order, multilayer, and Bayesian community detection.
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May 5, 2026
Infomap v2.10
Per-level module counts in JSON output, idiomatic R package with SWIG bindings, library-safe no-output mode (changelog).
May 5, 2026
Infomap is now on R-universe
After many requests, Infomap is now available as an idiomatic R package with
SWIG bindings. Install it from R-universe
and run multilevel community detection directly from R — including a
high-level cluster_infomap() helper that takes an edge list and returns
modules, codelength, and a tidy node table. This brings the same algorithm
and feature set as the Python API to the R community.
Feb 25, 2026
Infomap v2.9
Enhanced CLI summary output, codelength correction for higher-order solutions, NetworkX multilayer graph state ID handling (changelog).
Feb 3, 2026
Community Detection with the Map Equation and Infomap: Theory and Applications
Jan 23, 2026
Mapping memory-biased dynamics with compact models reveals overlapping communities in large networks
“The best maps convey a great deal of information but require minimal bandwidth: the best maps are also good compressions.”
M. Rosvall and C. T. Bergstrom, PNAS 105, 1118 (2008)